Comparative life cycle assessment of five different ...lcafood2014.org/papers/165.pdf ·...

Comparative life cycle assessment of five different vegetable oils

Ivan Muñoz*, Jannick Hoejrup Schmidt, Randi Dalgaard

2.-0 LCA consultants, Skibbrogade 5, 1, 9000, Aalborg, Denmark. Tel. +45 333 22822

* Corresponding author. E-mail: [email protected]

ABSTRACT

The purpose of this work was to evaluate from cradle to gate the environmental performance of five vegetable oils: palm oil, soybean oil,

rapeseed oil, sunflower oil and peanut oil, using a consequential approach and including indirect land use change. The impact assessment

focused on greenhouse-gas (GHG) emissions, land use and water consumption. For GHG emissions, rapeseed oil and sunflower oil were

the best performing ones, followed by soybean oil and palm oil and with peanut oil as the oil with the highest impact. Focusing on water

consumption, sunflower oil was the oil with the smallest impact, followed by rapeseed oil, palm oil and soybean oil, and with peanut oil

as the oil with the largest contribution. Regarding land use, palm oil and soybean oil were the oils associated with the smallest contribu-

tion, followed by rapeseed oil, and with sunflower oil and peanut oil as the oils with the largest net occupation of land.

Keywords: Palm oil, Soybean oil, Rapeseed oil, Sunflower oil, Peanut oil.

1. Introduction

The aim of this study was to evaluate the environmental impacts of a number of the major vegetable oils:

palm oil, soybean oil, rapeseed oil, sunflower oil and peanut oil. Different vegetable oils systems are associated

with different quantities of co-products, mainly oil meals, which are used as animal feed. When studying market

responses related to changes in demand for the different oils and when substituting different oils, it is a challenge

to address the interactions among oils and with the feed markets. The studied product systems were identified

using a systems perspective approach where likely market responses and substitution effects are considered.

Previous research on comparative life cycle information on vegetable oils is relatively limited. Examples are

Arvidssona et al. (2013) and Schmidt (2010). A larger number of studies exist within the field of biodiesel (e.g.

Menichetti et al. 2009), which are most often limited to focus only on GHG emissions compared to mineral die-

sel (Mentena et al. 2013).

2. Methods

2.1. Goal, scope and functional units

The purpose of the study was to obtain environmental information on different major vegetable oils, to pro-

vide decision support for situations where different vegetable oils can be used, i.e. where the oils are substituta-

ble. A functional unit of one ton refined (neutralized, bleached and deodorized; NBD) vegetable oil at refinery

gate was used.

2.2. Consequential life cycle inventory modelling

The consequential modeling principles are comprehensively described in Ekvall and Weidema (2004) and

Weidema et al. (2009). This approach was consistently applied throughout the study. The attributional approach

would fail to comply with the purpose of the study, which focuses on predicting the environmental consequences

of choosing different oils.

Production of refined vegetable oils is characterized by being associated with several by-products where the

major ones are the oil meals from the oil mills and free fatty acids (FFA), which are a by-product from the refin-

ing process. Both the oil meals and FFA are used as animal feed. Schmidt at Weidema (2008) identified two ma-

jor segments of the global generic animal feed market; namely feed protein and feed energy. Therefore a change

in supply of oil meals and FFA will substitute these two products in proportion with their protein and energy

content. Since the attributional approach only estimates/approximates the effects from downstream processing of

by-products and product substitutions by applying an allocation factor on the upstream effects, this approach

does not reflect actual cause-effect mechanisms in the market. The technique for performing the substitution cal-

Proceedings of the 9th International Conference on Life Cycle Assessment in the Agri-Food Sector

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culations for vegetable oil systems is demonstrated in several studies, e.g. Dalgaard et al (2008) and it is also im-

plemented in ecoinvent v3 (ecoinvent Centre 2013).

2.3. System boundaries

The inventories were established to represent 2011. The production functions for oil mill and refinery opera-

tions were regarded as being relatively constant over time, so data for 2005-2010 were used to represent 2011.

The study followed the same cut-off criteria as the ecoinvent v2.2 database (ecoinvent 2010). This implied

that inputs of services (such as cleaning, accounting, lawyers, marketing, business travelling), research and de-

veloping (laboratories, equipment, offices etc.), and overhead (overhead energy, office equipment etc.) were not

included. Further, the use of pesticides was not included in the study. Indirect land use changes (iLUC) were in-

cluded (see section 2.3).

The oils were inventoried from cradle to gate (at refinery). Each of the five oil product systems included three

product stages: 1) oil crop cultivation, 2) oil mill, and 3) refinery. Generally, the oil mills supply crude oil and oil

meal (feed energy and feed protein) and the refineries supply refined oil and free fatty acids (feed energy). How-

ever, the palm oil system includes an additional step, since the kernels from the fresh fruit bunches are sent to

another oil extraction step. As an example, Figure 1 shows the process diagram and mass balance for the palm

oil and rapeseed oil systems. When solving the inventory problems related to multiple product output systems

using substitution, it was necessary to identify which one of the co-products was the determining one. The latter

is defined as the one for which a change in demand leads to a change in supply. In short, for all oils, except the

soybean oil system, the oil was the determining co-product. For soybean oil though, it is the demand for the pro-

tein meal that determines the supply, not the demand for the oil. Therefore, the modeling of soybean oil did not

directly involve the cultivation and processing of soybeans. Instead, a change in demand for soybean oil would

affect other users of soybean oil, which would then have to compensate with another oil. The most likely com-

pensation oil was palm oil since this was identified as the marginal one (Schmidt and Weidema 2008). This

meant that the environmental impact from demanding soybean oil was similar to that of palm oil. The identifica-

tion of determining products and byproducts is summarized in Table 1.

Figure 1. Process diagrams for palm oil (left) and for rapeseed oil as an example of all other oils (right). NBD:

neutralized, bleached, deodorized, FFB: fresh fruit bunches, CPO: crude palm oil, CPKO crude palm kernel oil,

FFA: free fatty acids, PO: palm oil, PKO: palm kernel oil, PKM: palm kernel meal, RSM: rapeseed meal,

CRSO: crude rapeseed oil.


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Table 1. Determining products and by-products in the five oil production systems. Palm oil sys-

tem

Soybean oil

system

Rapeseed

oil system

Sunflower

oil system

Peanut oil

system

NBD oil Determining Byproduct Determining Determining Determining

NBD palm kernel oil Byproduct n.a. n.a. n.a. n.a.

Protein meal Byproduct Determining Byproduct Byproduct Byproduct

FFA Byproduct Byproduct Byproduct Byproduct Byproduct

n.a.: not applicable.

For the substituted systems caused by the by-products, the marginal supply of feed energy and feed protein

were identified as barley and soybean meal in Schmidt and Dalgaard (2012, 63-64, 85). Furthermore, Schmidt

and Dalgaard (2012), identified the marginal suppliers of barley and soybean meal as Ukraine and Brazil.

The inventoried regions of the five oils were identified as the country/region with the highest increasing trend

in production volume of the oil crops from 2001 to 2011 (FAOSTAT 2013a), as this trend indicates which re-

gion could be a good candidate for the marginal one, i.e. the country/region that is likely to supply a change in

demand for a specific oil. It was assumed that the oil mills and refineries are located in the same region/country

as the cultivation of the oil crops. The chosen regions were:

Fresh fruit bunches: Indonesia/Malaysia

Soybean: Brazil

Rapeseed: Europe (EU27)

Sunflower: Ukraine

Peanut: India

2.4. Indirect land use change (iLUC)

In the current study an advanced cause-effect based iLUC model described in Schmidt et al. (2012) is ap-

plied. This model was developed by 2.-0 LCA consultants through a larger project (2.-0 LCA consultants, 2014)

supported by a large range of industries (e.g. Unilever, DuPont, TetraPak, Arla Foods, DONG Energy, United

Plantations), universities (e.g. Swedish University of Agriculture Sciences, Aalborg University and Copenhagen

University) and other research related organisations (e.g. The Sustainability Consortium, the ecoinvent LCA da-

tabase, the Roundtable on Sustainable Palm Oil (RSPO) and the Japanese National Agricultural Research Cen-

ter) plus several others.

This model considers land as capacity for biomass production. There exists a market for land, which is called

the land tenure market. Since crops can be grown in different parts of the world and since crops are traded on

global markets, it is argued that this market for land is global. The ‘product’ traded on this global market is ca-

pacity for biomass production. It should be noted that this capacity can be created in different ways:

1. Expansion of the area of arable land (deforestation)

2. Intensification of land already in use (through e.g. increased use of fertilizers)

3. Crop displacement, i.e. someone reduces consumption, e.g. induced by increases in prices, in order to al-

low others for using the biomass production capacity (social impacts)

The third point above is assumed to be zero because LCA considers long-term effects of changes in demand.

In the long term, suppliers will adjust their production to match demand, and unless the production costs are

higher, the prices will remain unchanged.

The capacity for biomass production needs to be measured in an appropriate unit. An obvious option for a

reference flow of a land-tenure activity would be occupation of land (ha yr). However, this approach does not

take into account that the potential production on 1 ha yr land in e.g. a dry temperate climate is very different

from the potential in wet tropical climate. Another option would be the potential Net Primary Production (NPP0),

measured in kg carbon, which was the solution adopted.

As it can be seen in Figure 2, the model accounts for emissions related to both expansion (deforestation) and

intensification of land. The proportion between expansion and from intensification is calculated based on the to-

tal NPP0 on new arable land and total NPP0 (carbon in crops) from an increase in fertilizer use in one year. All

inflows to the land-market tenure activity are measured in kg NPP0 (as kg carbon). The NPP0 from expansion is

determined based on general NPP0 per ha yr figures (Haberl et al. 2007) and figures on annual increase of arable

land. The NPP0 from intensification is calculated as the carbon in crop produced via intensification during one


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year. The intensification is determined based on crop yield dose-response figures for fertilizer input (Schmidt

2008) combined with information on which crops and where intensification takes place (data from FAOSTAT)

and current fertilizer levels for these crops (IFA 2013).

Figure 2. Conceptual representation of the iLUC model used in the study.

When the occupation of land causes deforestation, a critical point is often to decide the period of time over

which the deforestation emissions should be allocated or 'amortized'. The current model does not operate with

amortization. If only expansion is considered, occupation of 1 ha in 1 year will cause 1 ha deforestation. After

the duration of 1 yr, the land is released to the market for land, i.e. to other crops, which can then be grown with-

out deforestation. Hence, the occupation of 1 ha yr is modeled as 1 ha deforestation in year 0 and -1 ha defor-

estation in year 1. In order to model the GHG effects of this intermediate acceleration of deforestation, the meth-

od described in Kløverpris and Mueller (2013) is used.

Overall this iLUC model has several key characteristics that make it superior to many of the other existing

models: is applicable to all crops (also forest, range, build etc.) in all regions in the world, it overcomes the allo-

cation/amortization of transformation impacts, and it is based on modeling assumptions that follow cause-effect

relationships and standard modeling consistent with any other LCA-processes. It is acknowledged though that

this is one among many other models, and that there is currently no consensus in the LCA community on how to

model iLUC. Therefore, the contributions to results from iLUC are reported separately.

2.5. Life cycle inventory data sources

The oil crop (and barley) yields were estimated for 2011 by linear regression over the period 2001-2011 with

use of data from FAOSTAT. For oil palm a weighted average of Malaysia and Indonesia was applied, based on

area cultivated: 40% MY and 60% ID. Fertilizer inputs are identified in the following data sources: Oil palm

(Schmidt 2007), Soybean (Dalgaard et al. 2008), rapeseed oil (Plantedirektoratet 2004), Sunflower (FAO 2005),

Peanut (Diwakar 2004; Talawar 2004), and Barley (FAO 2005). The fertilizer mixes of different sources of N, P

and K are identified at the country level in IFA (2013). Diesel consumption was obtained from Dalgaard et al.

(2008), Cederberg et al. (2009) and Schmidt (2007). Water use for irrigation was modeled using data from AQ-

UASTAT (FAOSTAT 2013b) and FAOSTAT (2013a). Oil palms are not irrigated and <1% of the area with

barley and sunflower in Ukraine is irrigated. Irrigation data for rapeseed in EU27 were approximated by non-

crop specific data for Germany and France. For the iLUC model, the potential productivity of the occupied land


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was estimated based on Haberl et al. (2007). N-related emissions for all crops (NH3, NOx, N2O, NO3, N2) were

estimated based on a N balance, using the methodology from IPCC (2006).

In average 18% of the cultivated oil palms were assumed to be grown on peat, based on Agus et al. (2013). It

should be noticed that this proportion may not reflect the marginal producers of palm oil, but the current average.

CO2 emissions from peat were estimated as 43 t CO2 per ha yr (for average drainage depth) based on a review of

several studies (Agus et al. 2013b; Melling and Henson 2011; Hooijer et al. 2010; Hooijer et al. 2012). N2O

emissions from peat oxidation were calculated by using IPCC (2006).

The inventories for oil extraction in the mills were based on Schmidt (2007), Dalgaard et al. (2008) and Dal-

gaard and Schmidt (2012) for soybean oil, palm oil, palm kernel oil and rapeseed oil. For sunflower and peanut

oils the meal/oil ratios were obtained using statistics on global oil and meal production from the period 00/01 to

05/06 (Oil World annual 2005). The milling operation includes thermal energy inputs, electricity inputs (as well

as outputs exported to the grid), transports, water inputs, and capital equipment (mill). The by-products from the

milling process e.g. empty fruit bunches, meal, are utilized as fertilizers or feed and thereby substitute mineral

fertilizer or feed energy (barley) and feed protein (soybean meal) on the market.

The inventories for the refining process are based on Schmidt (2007) and Schmidt and Dalgaard (2012).

These refineries supply refined oils and FFA (free fatty acids). The latter is used for livestock feeding and there-

by substitute feed energy on the market.

The ecoinvent database v.2.2 (ecoinvent 2010) was used to model the background system (production of fer-

tilizers, and other materials, energy, and capital goods. This database version is not linked using consequential

modeling. However electricity profiles used in the foreground system (Europe, India, Brazil, Indonesia, Malay-

sia, World) were defined using a consequential approach, as explained in Schmidt et al. (2011).

Inventory data for marginal sources of feed protein (soybean meal from Brazil) and feed energy (barley from

Ukraine) were obtained from Dalgaard and Schmidt (2012) and Schmidt and Dalgaard (2012).

2.6. Life cycle impact assessment

Life cycle impact assessment focused on three indicators, namely global warming, land occupation and water

consumption. Global warming is calculated using IPCC’s GWP100 (Forster et al. 2007). Biogenic CO2 was in-

cluded as CO2 stored in oils. Biogenic CO2 emissions originating from iLUC were also included. Land occupa-

tion is used in this study as an early midpoint indicator for impacts on biodiversity, and it includes only occupa-

tion of arable land. With regard to water consumption we used the terminology from the Water Footprint

network (Hoekstra et al. 2011), however only ’blue’ water (irrigation) is included in the impact assessment,

whereas ’green’ water (rain water) as well as ’grey’ water (pollution) were excluded. Water consumption was

assessed with two complementary approaches, namely consumption in volume (m3), and consumption after

characterization using the Water Stress Index (WSI) from Pfister et al. (2009), measured in m3-eq.

3. Results and discussion

3.1. Results per ton of oil

Figure 3 shows the results of the impact assessment. The results for soybean oil and palm oil are equal. This

is explained by the fact that soybean oil is a dependent co-product, thus an extra demand for soybean oil does not

affect the soybean oil production, but instead the production of palm oil. The GHG emissions include in all oils a

credit due to the embedded carbon in the oils, of 2.8 tons CO2 per ton oil. It is common in many studies to omit

this short-term storage of carbon in products, given that it is expected to be compensated by an equal emission at

the end of life. In order to compare our results with studies using this approach, the GWP results in Figure 1

would need to be changed by means of adding 2.8 tons CO2 per ton oil, for all oils.

When iLUC emissions are considered, the overall GHG emissions are considerably higher for peanut oil

compared to the others. This is mainly because the land occupation per ton refined oil is high (as shown in the

land occupation graph in Figure 1). High land occupation results in high iLUC-related GHG emissions. When

iLUC emissions are excluded, the overall GHG emissions are reduced for all oils, and the ranking changes

slightly, with sunflower oil as the best performer, whereas when iLUC is included rapeseed oil is the best per-

former. For the other oils, iLUC does not affect the ranking.


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The results for blue water use show that palm oil, soybean oil and peanut oil have a net consumption of water

while rapeseed oil and sunflower oil are associated with a net water saving. This may seem surprising because

both rapeseed and sunflower are irrigated. However, the by-products of rapeseed oil and sunflower oil, i.e. the

oil meals, displace soybean meal, barley, and palm oil. Since soybeans and barley are irrigated more than rape-

seed and sunflower, the net use of water becomes negative. Palm oil is associated with a low water use because it

is not irrigated, however it is not zero due to contributions from the oil mill, refineries, production of chemicals,

fertilizers, machinery, etc. When the WSI is applied, it does not change the ranking of oils as compared to blue

water by volume.

Figure 3. Results for five different refined vegetable oils, per ton refined oil. Global Warming includes biogenic

carbon sequestration in the oil.

With regard to land occupation, the highest contribution for all oils is caused by the crop from which each of

the oils is extracted. Other contributions are related to substitutions caused by by-products. Even accounting for

substitutions, it is the land occupied by the oil crop itself the one that determines the main impact. The best-

performing oils are palm oil and soybean oil, followed by rapeseed oil. Sunflower oil and peanut oil, in this or-

der, are the ones with the highest land occupation.


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3.2. Sensitivity analyses focused on GHG emissions

A number of sensitivity analyses were carried out, with a focus on GHG emissions. They are summarized in

Table 2.

Table 2. Sensitivity analysis summary. Parameter Description Sensitivity analysis outcome

Palm oil: FFB yields Oil palm is cultivated with Malaysian and Indonesian yields re-

spectively, rather than weighted average.

Low sensitivity, ranking un-

changed

Palm oil: Share of oil palm area

on peat soils

Default share of peat (18%) compared to cultivation on mineral

soil (0% peat), 11% peat (Malaysian average) and 22% (Indone-

sian average).

Low peat share leads to palm

oil with similar GHG emis-

sions to rapeseed oil

Palm oil: CO2 emission factor

for peat soils

Default emission factor of 43 ton CO2/ha yr compared to 27.5 and

85.5 ton CO2/ha yr

Sensitive but ranking un-

changed

All oils: Fertilizer manufactur-

ing (nitrous oxide emissions

from nitric acid)

Default emission factor is 8.39 g N2O/kg HNO3 compared with

4.5 g N2O/kg HNO3 (IPCC default value) and 1 g N2O/kg HNO3

(representing best available technique).


changed

All oils: Source of marginal bar-

ley (feed energy)

Marginal supplier of barley (Ukraine) changed to Argentina. Low sensitivity, ranking un-

changed

All oils: iLUC model Default iLUC proportion between transformation and intensifica-

tion is 37% and 63%, respectively. This is changed to transfor-

mation and intensification being 90% and 10%, respectively.


changed

The most significant individual sources of uncertainty related to GHG emissions were identified as the share

of oil palm grown on peat, and the CO2 emission factors for peat soils. If the share of peat is very small, the

GHG emissions for palm oil become close to rapeseed oil. In the future, when the palm oil expansion may take

place in other regions than Indonesia and Malaysia, the issue of peat may change if this will take place in e.g.

Africa or Latin America.

3.3. Uncertainties in water use

The data used for water consumption are often not crop-specific. Hence, these data are associated with signif-

icant uncertainties. Substantial uncertainty is also associated with the determination of WSI values, given that

the exact location where crops are grown is not known in detail, and average values for regions, countries or

groups of countries need to be used.

4. Conclusion

A consequential cradle-to-gate LCA has been carried out on five major vegetable oils. The environmental

impacts assessed were GHG emissions, water consumption and land occupation. When we look at the environ-

mental impacts of demanding an additional ton of oils, we see clear tradeoffs. Rapeseed oil and sunflower oil

perform best in GHG emissions, sunflower oil in water consumption and palm oil and soybean oil in land use.

The poor performance of peanut oil stands out in all impact indicators.

It was found that iLUC emissions have a substantial contribution to GHG emissions, especially in those oils

with relatively low yields. As for water consumption, the use of WSI factors didn’t change the ranking of oils,

but mainly increased the differences between them.

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894

This paper is from:

Proceedings of the 9th International Conference on

Life Cycle Assessment in the Agri-Food Sector

8-10 October 2014 - San Francisco

Rita Schenck and Douglas Huizenga, Editors

American Center for Life Cycle Assessment

The full proceedings document can be found here:

http://lcacenter.org/lcafood2014/proceedings/LCA_Food_2014_Proceedings.pdf

It should be cited as:

Schenck, R., Huizenga, D. (Eds.), 2014. Proceedings of the 9th International Conference on Life

Cycle Assessment in the Agri-Food Sector (LCA Food 2014), 8-10 October 2014, San Francisco,

USA. ACLCA, Vashon, WA, USA.

Questions and comments can be addressed to: [email protected]

ISBN: 978-0-9882145-7-6

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